code: def get_preprocess_fns(model_cfg, tokenizer=None, is_train=True):
      # Define transforms
      img_size = (
          model_cfg.vision.get("img_h"),
          model_cfg.vision.get("img_w"),
      )
      img_mean = model_cfg.vision.get("img_mean")
      img_std = model_cfg.vision.get("img_std")
      img_transform = image_transform(
          img_size,
          is_train=is_train,
          mean=img_mean,
          std=img_std,
      )
      text_transform = lambda x: x
      if tokenizer is not None:
          text_transform = partial(
              tokenize,
              tokenizer=tokenizer,
              context_length=model_cfg.text.get(
                  "max_position_embeddings"
              ),
          )
      return img_transform, text_transform
  
file_path: NeMo/nemo/collections/multimodal/data/clip/clip_dataset.py
imports: from functools import partial
  from typing import Any, List, Union
  import torch
  from torch.utils.data import Dataset, default_collate
  from nemo.collections.multimodal.data.clip.augmentations.augmentations import image_transform
  from nemo.collections.multimodal.data.clip.imagenet_zeroshot_data import imagenet_classnames, openai_imagenet_template
  from nemo.collections.multimodal.data.common.webdataset import WebDatasetCommon
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingSampler
  from nemo.collections.vision.data.megatron.image_folder import ImageFolder
label_desc: Use image node_id 2 for calling a global function with example usage: get_preprocess_fns(model_cfg, tokenizer, is_train) and returns: img_transform,  text_transform